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7th International Conference on Medical and Health informatics, ICMHI 2023

作者： Li, Ai-Hsien Adams Nguyen, Duc-Khanh Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Yang, Pan-Chyr Division of Cardiology Far Eastern Memorial Hospital Taipei220 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Information Management Yuan Ze University Taoyuan320 Taiwan Department of Radiology Far Eastern Memorial Hospital Taipei220 Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital Taipei Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan320 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei106 Taiwan Department of Internal Medicine College of Medicine National Taiwan University Taipei100 Taiwan Center of Genomic Medicine National Taiwan University Taipei100 Taiwan

ISBN: (纸本)9798400700712

Lung cancer remains a significant global public health concern, being the leading cause of cancer-related deaths worldwide. Despite recent medical advancements, the disease still has a high mortality rate, making early detection and treatment critical for improving patient outcomes. Accurate prediction of nodule growth is key to early lung cancer treatment, but current assessments offer unclear indications of future growth, leading physicians to recommend costly and anxiety-provoking follow-up appointments every 3 to 12 months. To address this need, this study develops an ensemble deep learning approach that combines demographic data with existing CT images to predict whether lung nodules will grow, potentially reducing unnecessary examinations and easing the burden on patients. Our study, conducted on 862 patients with 1004 nodules, produced promising preliminary results with Accuracy, Sensitivity, Precision, F1 Score, and AUC of 0.66, 0.66, 0.67, 0.66, and 0.71, respectively. The proposed method provides a promising support system to empower patients to make informed decisions about seeking medical attention and helps physicians facilitate early treatment regimens, leading to improved patient outcomes and potentially saving lives. © 2023 ACM.

关键词： Forecasting

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Exploring the Roles of Agents and Multi-Agent in Improving Mobile Ad Hoc Networks 4

Exploring the Roles of Agents and Multi-Agent in Improving M...

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4th International Symposium on Agents, Multi-Agents Systems and Robotics, ISAMSR 2021

作者： Jubair, Mohammed Ahmed Alabdullah, Abdullah I. M. Mostafa, Salama A. Mustapha, Aida Hassan, Mustafa Hamid Gunasekaran, Saraswathy Shamini College of Information Technology Imam ja'Afar Al-Sadiq University Department of Computer Technical Engineering Al-Muthanna Iraq University of Mosul College of Science Department of Physics Mosul Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia College of Computer Science and Informatics Universiti Tenaga Nasional Kajang43000 Malaysia

ISBN: (纸本)9781728166544

We are experiencing a rapid evolution and advancement of technologies that are capable of supporting the formulation and implementation of robust and adaptive network architectures designed for mobile and dynamic environments. An example of such technologies is the Mobile Ad-Hoc Network (MANET), which supports self-organizing multi-hop heterogeneous network routing services and organization. The importance of such technologies for the future of DoD networking is evident, particularly in the front edge of the battlespace where there is a need for robust and self-organizing network. Another area of such technological advancement is the Multi-Agent Systems (MAS), which are capable of supporting both team-based and independent problem solving in many different environments. These two technologies have been extensively explored, but their combination is largely unexplored. Consequently, this paper aims to investigate the joint operation of agents and multi-agent in the MANET environment. The paper also discusses the research efforts that have been made to optimally improve the technologies. © 2021 IEEE.

关键词： Multi agent systems

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Simon’s Period Finding on a Quantum Annealer

arXiv

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arXiv 2025年

作者： Robertson, Reece Doucet, Emery Mzaouali, Zakaria Domino, Krzysztof Gardas, Bartlomiej Deffner, Sebastian Department of Computer Science & Electrical Engineering Department of Physics Quantum Science Institute UMBC Baltimore United States Department of Physics Quantum Science Institute UMBC Baltimore United States Institut für Theoretische Physik Universität Tübingen Auf der Morgenstelle 14 Tübingen72076 Germany Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland National Quantum Laboratory College Park United States

Dating to 1994, Simon’s period-finding algorithm is among the earliest and most fragile of quantum algorithms. The algorithm’s fragility arises from the requirement that, to solve an n qubit problem, one must fault-tolerantly sample O(n) linearly independent values from a solution space. In this paper, we study an adiabatic implementation of Simon’s algorithm that requires a constant number of successful samples regardless of problem size. We implement this algorithm on D-Wave hardware and solve problems with up to 298 qubits. We compare the runtime of classical algorithms to the D-Wave solution to analyze any potential advantage. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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A Machine Learning Approach for Analyzing Texture and Morphometry Markers of Glioblastoma TERT Promoter Mutations in DAPI Fluorescence Images

A Machine Learning Approach for Analyzing Texture and Morpho...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Afsari, Fatemeh Naglah, Ahmed Gupta, Akshita Lohaan, Harshit Onubogu, Ugoma Janiszewska, Michalina Sarder, Pinaki Division of Nephrology Hypertension and Renal Transplantation-Quantitative Health Section Department of Medicine College of Medicine University of Florida GainesvilleFL32603 United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida GainesvilleFL32611 United States Department of Computer and Information Science and Engineering University of Florida GainesvilleFL32611 United States Department of Molecular Medicine The Herbert Wertheim UF Scripps Institute for Biomedical Innovation and Technology JupiterFL33458 United States

ISBN: (纸本)9781510686045

Advanced molecular and imaging techniques, such as STAR-FISH, are frequently required to identify mutations in tissue specimens to properly diagnose Glioblastoma (GBM), an aggressive type of brain tumor. Mutations to the telomerase reverse transcriptase promoter (TERTp) are common in cancer and present in approximately 80% of all GBM tumors. We hypothesize that machine learning can stratify cells by their TERTp mutation status using DAPI fluorescence images since the STAR-FISH technique can be costly and inaccessible for some healthcare facilities. In this study, we present a machine learning-based approach to recognize nuclei harboring TERTp mutations solely based on the DAPI fluorescence images of GBM tissues. The proposed pipeline integrates a convolutional neural network (CNN)-based deep learning model to segment cells, extract hand-engineered features, and feed them into fully connected (FC) layers within the neural network or other machine learning classifiers to identify TERTp mutant cells. This approach involves extracting DAPI features, including both morphological and textural image features, followed by an ensemble of machine learning modules to predict TERTp mutant cells. We evaluate our approach on a cohort of 18 patients (70 pairs of DAPI and STAR-FISH processed images). Our experimental results show that the FC layers within the neural network achieve the highest AUC, F1-Score, and recall of 0.77, 0.69, and 0.86, respectively, with the integrated CNN model achieving a Dice score of 0.83, accurately segmenting cells. Our work is one of the earliest attempts that we are aware of to use machine-learning models for TERTp mutation identification using DAPI fluorescence images. Our results indicate the possibility of improved GBM diagnosis accuracy. To improve patient diagnosis and treatment planning for GBM, future research aims will expand the cohort and enhance the models to provide more precise and diagnostically valuable models for the interpretation o

关键词： Image segmentation

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Semi-automated spine and intervertebral disk detection and segmentation from whole spine MR images

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informatics in Medicine Unlocked 2021年 27卷

作者： Badarneh, Alaa Abu-Qasmeih, Isam Otoom, Mwaffaq Alzubaidi, Mohammad A. Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid 21163 Jordan Department of Computer Engineering Yarmouk University Irbid 21163 Jordan

Intervertebral disks are found between the vertebrae in the spine from the second cervical vertebra to the sacrum. Intervertebral disks permit various movements of the vertebral column to keep it flexible and to absorb vertical shock. The spine can be exposed to several types of compression forces that can adversely affect disk function and cause diseases or disorders, such as herniated disks. Mainly to diagnose these disorders we use Magnetic Resonance Imaging (MRI), since it's the preferred method for extracting the spinal information needed. In this paper, a computer Aided Diagnosis (CAD) system for intervertebral disk detection and segmentation, using sagittal MR images of the whole spine has been proposed. The system consists of three major steps as follows: spine region extraction, intervertebral disk detection, and intervertebral disk segmentation. A 6th order polynomial Iterative Randomized Hough Transform (IRHT) approach is used to segment the spine sub-image from a whole spine T1/T2-weighted MR image in a relatively short time. Finally, an elliptical Hough Transform (HT) is used to detect the intervertebral disks from the segmented spine image. The proposed system used a whole spine images dataset consisting of 31 spine T1/T2 MR images. The accuracy measures of intervertebral disk detection and disk segmentation are 94.65% and 90%, respectively. The results show that the proposed system is robust and accurate for spine segmentation and intervertebral disks detection with high performance. Finally, the proposed system is a useful tool for radiologists in detecting as well as segmenting intervertebral disk in whole spine MR images. © 2021 The Authors

关键词： Detection Intervertebral disk Polynomial IRHT Segmentation T2-weighted MR image Whole spine

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Unveiling the Post-Covid Economic Impact Using NLP Techniques

Unveiling the Post-Covid Economic Impact Using NLP Technique...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Kanishk Barhanpurkar Nikita Mandlik Anand Singh Rajawat S.B. Goyal Traian Candin Mihaltan Chaman Verma Maria Simona Raboaca Thomas J. Watson College of Engineering and Applied Science Binghamton University USA School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania

The research paper presents a novel analysis of textual data based on Natural Language Processing (NLP) techniques to analyse New York Times articles from January 2019 to May 2023. The purpose of this paper is to gain an understanding of the economic impact that follows Covid-19 disease. New York Times (NYT), it was started in 1884 is one of the most prominent newspapers in the world. Additionally, we have used the New York Times Archive API to collect the data for the given timeframe. By analysing sentiment analysis, topic modelling, entity recognition, and keyword extraction, valuable insights can be gathered into market trends, industry shifts, and policy interventions. The authors have created a data science pipeline using Amazon Web Services (AWS), which enables data collection, storage, and visualization. It contributes to a better understanding of the pandemic's short-term and long-term economic effects. The results of this study demonstrate Natural Language Processing techniques' potential as a tool for financial analytics, assisting policymakers, economists, and businesses in formulating recovery strategies.

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Error-based Knockoffs Inference for Controlled Feature Selection

arXiv

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arXiv 2022年

作者： Zhao, Xuebin Chen, Hong Wang, Yingjie Li, Weifu Gong, Tieliang Wang, Yulong Zheng, Feng College of Science Huazhong Agricultural University Wuhan430062 China College of Informatics Huazhong Agricultural University Wuhan430062 China School of Computer Science and Technology Xi'An Jiaotong University Xi'An710049 China Department of Computer Science and Engineering Southern University of Science and Technology

Recently, the scheme of model-X knockoffs was proposed as a promising solution to address controlled feature selection under high-dimensional finite-sample settings. However, the procedure of model-X knockoffs depends heavily on the coefficient-based feature importance and only concerns the control of false discovery rate (FDR). To further improve its adaptivity and flexibility, in this paper, we propose an error-based knockoff inference method by integrating the knockoff features, the error-based feature importance statistics, and the stepdown procedure together. The proposed inference procedure does not require specifying a regression model and can handle feature selection with theoretical guarantees on controlling false discovery proportion (FDP), FDR, or k-familywise error rate (k-FWER). Empirical evaluations demonstrate the competitive performance of our approach on both simulated and real data. © 2022, CC BY-NC-ND.

关键词： Feature extraction

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Conditional score-based diffusion models for solving inverse problems in mechanics

arXiv

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arXiv 2024年

作者： Dasgupta, Agnimitra Ramaswamy, Harisankar Murgoitio-Esandi, Javier Foo, Ken Li, Runze Zhou, Qifa Kennedy, Brendan F. Oberai, Assad A. Department of Aerospace & Mechanical Engineering University of Southern California Los AngelesCA90089 United States BRITElab Harry Perkins Institute of Medical Research QEII Medical Centre NedlandsWA6009 Australia Centre for Medical Research The University of Western Australia PerthWA6009 Australia Department of Electrical Electronic & Computer Engineering School of Engineering The University of Western Australia PerthWA6009 Australia Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Toruń Grudziadzka 5 Torun87-100 Poland Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Alfred E. Mann Department of Biomedical Engineering University of Southern California Los AngelesCA90089 United States

We propose a framework to perform Bayesian inference using conditional score-based diffusion models to solve a class of inverse problems in mechanics involving the inference of a specimen’s spatially varying material properties from noisy measurements of its mechanical response to loading. Conditional score-based diffusion models are generative models that learn to approximate the score function of a conditional distribution using samples from the joint distribution. More specifically, the score functions corresponding to multiple realizations of the measurement are approximated using a single neural network, the so-called score network, which is subsequently used to sample the posterior distribution using an appropriate Markov chain Monte Carlo scheme based on Langevin dynamics. Training the score network only requires simulating the forward model. Hence, the proposed approach can accommodate black-box forward models and complex measurement noise. Moreover, once the score network has been trained, it can be re-used to solve the inverse problem for different realizations of the measurements. We demonstrate the efficacy of the proposed approach on a suite of high-dimensional inverse problems in mechanics that involve inferring heterogeneous material properties from noisy measurements. Some examples we consider involve synthetic data, while others include data collected from actual elastography experiments. Further, our applications demonstrate that the proposed approach can handle different measurement modalities, complex patterns in the inferred quantities, non-Gaussian and non-additive noise models, and nonlinear black-box forward models. The results show that the proposed framework can solve large-scale physics-based inverse problems efficiently. © 2024, CC BY.

关键词： Markov chains

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Patient-Specific CBCT Synthesis for Real-time Tumor Tracking in Surface-guided Radiotherapy

arXiv

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arXiv 2024年

作者： Pan, Shaoyan Su, Vanessa Peng, Junbo Li, Junyuan Gao, Yuan Chang, Chih-Wei Wang, Tonghe Tian, Zhen Yang, Xiaofeng Department of Radiation Oncology Winship Cancer Institute Emory University AtlantaGA30322 United States Department of Computer Science and Informatics Emory University AtlantaGA30322 United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD21205 United States Department of Medical Physics Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Department of Radiation and Cellular Oncology University of Chicago ChicagoIL60637 United States

In this work, we present a new imaging system to support real-time tumor tracking for surface-guided radiotherapy (SGRT). SGRT uses optical surface imaging (OSI) to acquire real-time surface topography images of the patient on the treatment couch. This serves as a surrogate for intra-fractional tumor motion tracking to guide radiation delivery. However, OSI cannot visualize internal anatomy, leading to motion tracking uncertainties for internal tumors, as body surface motion often does not have a good correlation with the internal tumor motion, particularly for lung cancer. This study proposes an Advanced Surface Imaging (A-SI) framework to address this issue. In the proposed A-SI framework, a high-speed surface imaging camera consistently captures surface images during radiation delivery, and a CBCT imager captures single-angle X-ray projections at low frequency. The A-SI then utilizes a generative model to generate real-time volumetric images with full anatomy, referred to as Optical Surface-Derived cone beam computed tomography (OSD-CBCT), based on the real-time high-frequent surface images and the low-frequency collected single-angle X-ray projections. The generated OSD-CBCT can provide accurate tumor motion for precise radiation delivery. The A-SI framework uses a patient-specific generative model: physics-integrated consistency-refinement denoising diffusion probabilistic model (PC-DDPM). This model leverages patient-specific anatomical structures and respiratory motion patterns derived from four-dimensional CT (4DCT) during treatment planning. It then employs a geometric transformation module (GTM) to extract volumetric anatomy information from the single-angle X-ray projection. A physics-integrated and cycle-consistency refinement strategy uses this information and the surface images to guide the DDPM, generating high quality OSD-CBCTs throughout the entire radiation delivery. A simulation study with 22 lung cancer patients evaluated the A-SI framework suppo

关键词： computerized tomography

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Compositional construction of abstractions for infinite networks of discrete-time switched systems

arXiv

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arXiv 2021年

作者： Sharifi, Maryam Swikir, Abdalla Noroozi, Navid Zamani, Majid School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical and Computer Engineering Technical University of Munich Germany Institute for Informatics LMU Munich Germany Computer Science Department University of Colorado Boulder United States

In this paper, we develop a compositional scheme for the construction of continuous approximations for interconnections of infinitely many discrete-time switched systems. An approximation (also known as abstraction) is itself a continuous-space system, which can be used as a replacement of the original (also known as concrete) system in a controller design process. Having designed a controller for the abstract system, it is refined to a more detailed one for the concrete system. We use the notion of so-called simulation functions to quantify the mismatch between the original system and its approximation. In particular, each subsystem in the concrete network and its corresponding one in the abstract network are related through a notion of local simulation functions. We show that if the local simulation functions satisfy certain small-gain type conditions developed for a network containing infinitely many subsystems, then the aggregation of the individual simulation functions provides an overall simulation function quantifying the error between the overall abstraction network and the concrete one. In addition, we show that our methodology results in a scale-free compositional approach for any finite-but-arbitrarily large networks obtained from truncation of an infinite network. We provide a systematic approach to construct local abstractions and simulation functions for networks of linear switched systems. The required conditions are expressed in terms of linear matrix inequalities that can be efficiently computed. We illustrate the effectiveness of our approach through an application to AC islanded microgirds. Copyright © 2021, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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